Fix grammatical typos

docs/samples/Microsoft.ML.Samples/Dynamic/SimpleDataViewImplementation.cs

@@ -44,7 +44,7 @@ public static void Example()
 
             {
                 // Note that it is best to get the getters and values *before*
-                // iteration, so as to faciliate buffer sharing (if applicable),
+                // iteration, so as to facilitate buffer sharing (if applicable),
                 // and column-type validation once, rather than many times.
                 ReadOnlyMemory<char> textValue = default;
                 VBuffer<ReadOnlyMemory<char>> tokensValue = default;

docs/samples/Microsoft.ML.Samples/Dynamic/TensorFlow/TextClassification.cs

@@ -54,7 +54,7 @@ public static void Example()
                );
 
             // Load the TensorFlow model once.
-            //      - Use it for quering the schema for input and output in the
+            //      - Use it for querying the schema for input and output in the
             //            model
             //      - Use it for prediction in the pipeline.
             // Unfrozen (SavedModel format) models are loaded by providing the
@@ -78,7 +78,7 @@ public static void Example()
             // In this sample, CustomMappingEstimator is used to resize variable
             // length vector to fixed length vector.
             // The following ML.NET pipeline
-            //      1. tokenzies the string into words, 
+            //      1. tokenizes the string into words, 
             //      2. maps each word to an integer which is an index in the
             //         dictionary ('lookupMap'),
             //      3. Resizes the integer vector to a fixed length vector using

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/AnomalyDetection/RandomizedPcaSample.cs

@@ -27,7 +27,7 @@ public static void Example()
                 new DataPoint(){ Features = new float[3] {2, 0, 0} }
             };
 
-            // Convert the List<DataPoint> to IDataView, a consumble format to
+            // Convert the List<DataPoint> to IDataView, a consumable format to
             // ML.NET functions.
             var data = mlContext.Data.LoadFromEnumerable(samples);
 

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/AnomalyDetection/RandomizedPcaSampleWithOptions.cs

@@ -29,7 +29,7 @@ public static void Example()
                 new DataPoint(){ Features = new float[3] {1, 0, 0} }
             };
 
-            // Convert the List<DataPoint> to IDataView, a consumble format to
+            // Convert the List<DataPoint> to IDataView, a consumable format to
             // ML.NET functions.
             var data = mlContext.Data.LoadFromEnumerable(samples);
 
@@ -76,14 +76,14 @@ public static void Example()
                         i, featuresInText, result.Score);
             }
             // Lines printed out should be
-            // The 0 - th example with features[0, 2, 1] isan inlier with a score of being outlier 0.2264826
-            // The 1 - th example with features[0, 2, 3] isan inlier with a score of being outlier 0.1739471
-            // The 2 - th example with features[0, 2, 4] isan inlier with a score of being outlier 0.05711612
-            // The 3 - th example with features[0, 2, 1] isan inlier with a score of being outlier 0.2264826
-            // The 4 - th example with features[0, 2, 2] isan inlier with a score of being outlier 0.3868995
-            // The 5 - th example with features[0, 2, 3] isan inlier with a score of being outlier 0.1739471
-            // The 6 - th example with features[0, 2, 4] isan inlier with a score of being outlier 0.05711612
-            // The 7 - th example with features[1, 0, 0] isan outlier with a score of being outlier 0.6260795
+            // The 0 - th example with features[0, 2, 1] is an inlier with a score of being outlier 0.2264826
+            // The 1 - th example with features[0, 2, 3] is an inlier with a score of being outlier 0.1739471
+            // The 2 - th example with features[0, 2, 4] is an inlier with a score of being outlier 0.05711612
+            // The 3 - th example with features[0, 2, 1] is an inlier with a score of being outlier 0.2264826
+            // The 4 - th example with features[0, 2, 2] is an inlier with a score of being outlier 0.3868995
+            // The 5 - th example with features[0, 2, 3] is an inlier with a score of being outlier 0.1739471
+            // The 6 - th example with features[0, 2, 4] is an inlier with a score of being outlier 0.05711612
+            // The 7 - th example with features[1, 0, 0] is an outlier with a score of being outlier 0.6260795
         }
 
         // Example with 3 feature values. A training data set is a collection of

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/Calibrators/Isotonic.cs

@@ -55,7 +55,7 @@ public static void Example()
 
             var calibratorTransformer = calibratorEstimator.Fit(scoredData);
 
-            // Transform the scored data with a calibrator transfomer by adding a
+            // Transform the scored data with a calibrator transformer by adding a
             // new column names "Probability". This column is a calibrated version
             // of the "Score" column, meaning its values are a valid probability
             // value in the [0, 1] interval representing the chance that the

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FieldAwareFactorizationMachine.cs

@@ -93,11 +93,11 @@ public static void Example()
             }
 
             // Expected output:
-            //   Actual label: True, predicted label: True, score of being positive class: 1.115094, and probability of beling positive class: 0.7530775.
-            //   Actual label: False, predicted label: False, score of being positive class: -3.478797, and probability of beling positive class: 0.02992158.
-            //   Actual label: True, predicted label: True, score of being positive class: 3.191896, and probability of beling positive class: 0.9605282.
-            //   Actual label: False, predicted label: False, score of being positive class: -3.400863, and probability of beling positive class: 0.03226851.
-            //   Actual label: True, predicted label: True, score of being positive class: 4.06056, and probability of beling positive class: 0.9830528.
+            //   Actual label: True, predicted label: True, score of being positive class: 1.115094, and probability of being positive class: 0.7530775.
+            //   Actual label: False, predicted label: False, score of being positive class: -3.478797, and probability of being positive class: 0.02992158.
+            //   Actual label: True, predicted label: True, score of being positive class: 3.191896, and probability of being positive class: 0.9605282.
+            //   Actual label: False, predicted label: False, score of being positive class: -3.400863, and probability of being positive class: 0.03226851.
+            //   Actual label: True, predicted label: True, score of being positive class: 4.06056, and probability of being positive class: 0.9830528.
         }
 
         // Number of features per field.

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FieldAwareFactorizationMachine.tt

@@ -25,11 +25,11 @@ string TrainerDescription = @"// This trainer trains field-aware factorization (
 string TrainerOptions = null;
 
 string ExpectedOutputPerInstance= @"// Expected output:
-            //   Actual label: True, predicted label: True, score of being positive class: 1.115094, and probability of beling positive class: 0.7530775.
-            //   Actual label: False, predicted label: False, score of being positive class: -3.478797, and probability of beling positive class: 0.02992158.
-            //   Actual label: True, predicted label: True, score of being positive class: 3.191896, and probability of beling positive class: 0.9605282.
-            //   Actual label: False, predicted label: False, score of being positive class: -3.400863, and probability of beling positive class: 0.03226851.
-            //   Actual label: True, predicted label: True, score of being positive class: 4.06056, and probability of beling positive class: 0.9830528.";
+            //   Actual label: True, predicted label: True, score of being positive class: 1.115094, and probability of being positive class: 0.7530775.
+            //   Actual label: False, predicted label: False, score of being positive class: -3.478797, and probability of being positive class: 0.02992158.
+            //   Actual label: True, predicted label: True, score of being positive class: 3.191896, and probability of being positive class: 0.9605282.
+            //   Actual label: False, predicted label: False, score of being positive class: -3.400863, and probability of being positive class: 0.03226851.
+            //   Actual label: True, predicted label: True, score of being positive class: 4.06056, and probability of being positive class: 0.9830528.";
 
 string ExpectedOutput = @"// Expected output:
             //   Accuracy: 0.99

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FieldAwareFactorizationMachineWithOptions.cs

@@ -104,11 +104,11 @@ public static void Example()
             }
 
             // Expected output:
-            //   Actual label: True, predicted label: True, score of being positive class: 1.115094, and probability of beling positive class: 0.7530775.
-            //   Actual label: False, predicted label: False, score of being positive class: -3.478797, and probability of beling positive class: 0.02992158.
-            //   Actual label: True, predicted label: True, score of being positive class: 3.191896, and probability of beling positive class: 0.9605282.
-            //   Actual label: False, predicted label: False, score of being positive class: -3.400863, and probability of beling positive class: 0.03226851.
-            //   Actual label: True, predicted label: True, score of being positive class: 4.06056, and probability of beling positive class: 0.9830528.
+            //   Actual label: True, predicted label: True, score of being positive class: 1.115094, and probability of being positive class: 0.7530775.
+            //   Actual label: False, predicted label: False, score of being positive class: -3.478797, and probability of being positive class: 0.02992158.
+            //   Actual label: True, predicted label: True, score of being positive class: 3.191896, and probability of being positive class: 0.9605282.
+            //   Actual label: False, predicted label: False, score of being positive class: -3.400863, and probability of being positive class: 0.03226851.
+            //   Actual label: True, predicted label: True, score of being positive class: 4.06056, and probability of being positive class: 0.9830528.
         }
 
         // Number of features per field.

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FieldAwareFactorizationMachineWithOptions.tt

@@ -32,11 +32,11 @@ string TrainerOptions = @"FieldAwareFactorizationMachineTrainer.Options
             }";
 
 string ExpectedOutputPerInstance= @"// Expected output:
-            //   Actual label: True, predicted label: True, score of being positive class: 1.115094, and probability of beling positive class: 0.7530775.
-            //   Actual label: False, predicted label: False, score of being positive class: -3.478797, and probability of beling positive class: 0.02992158.
-            //   Actual label: True, predicted label: True, score of being positive class: 3.191896, and probability of beling positive class: 0.9605282.
-            //   Actual label: False, predicted label: False, score of being positive class: -3.400863, and probability of beling positive class: 0.03226851.
-            //   Actual label: True, predicted label: True, score of being positive class: 4.06056, and probability of beling positive class: 0.9830528.";
+            //   Actual label: True, predicted label: True, score of being positive class: 1.115094, and probability of being positive class: 0.7530775.
+            //   Actual label: False, predicted label: False, score of being positive class: -3.478797, and probability of being positive class: 0.02992158.
+            //   Actual label: True, predicted label: True, score of being positive class: 3.191896, and probability of being positive class: 0.9605282.
+            //   Actual label: False, predicted label: False, score of being positive class: -3.400863, and probability of being positive class: 0.03226851.
+            //   Actual label: True, predicted label: True, score of being positive class: 4.06056, and probability of being positive class: 0.9830528.";
 
 string ExpectedOutput = @"// Expected output:
             //   Accuracy: 0.99

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/MultipleFeatureColumnsBinaryClassification.ttinclude

@@ -93,7 +93,7 @@ namespace Samples.Dynamic.Trainers.BinaryClassification
             [VectorType(featureLength)]
             public float[] Field1 { get; set; }
 
-            // Features from the thrid field. 
+            // Features from the third field. 
             [VectorType(featureLength)]
             public float[] Field2 { get; set; }
         }

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/ImageClassification/ResnetV2101TransferLearningTrainTestSplit.cs

@@ -339,7 +339,7 @@ public class InMemoryImageData
             public string Label;
         }
 
-        // ImageData class holding the imagepath and label.
+        // ImageData class holding the image path and label.
         public class ImageData
         {
             [LoadColumn(0)]

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/PermutationFeatureImportanceLoadFromDisk.cs

@@ -57,7 +57,7 @@ public static void Example()
 
             // Now let's look at which features are most important to the model
             // overall. Get the feature indices sorted by their impact on
-            // microaccuracy.
+            // micro-accuracy.
             var sortedIndices = permutationMetrics
                 .Select((metrics, index) => new { index, metrics.MicroAccuracy })
                 .OrderByDescending(feature => Math.Abs(feature.MicroAccuracy.Mean))

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorization.cs

@@ -71,7 +71,7 @@ public static void Example()
             //   Mean Absolute Error: 0.67:
             //   Mean Squared Error: 0.79
             //   Root Mean Squared Error: 0.89
-            //   RSquared: 0.61 (closer to 1 is better. The worest case is 0)
+            //   RSquared: 0.61 (closer to 1 is better. The worst case is 0)
         }
 
         // The following variables are used to define the shape of the example

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorization.tt

@@ -27,5 +27,5 @@ string ExpectedOutput = @"// Expected output:
             //   Mean Absolute Error: 0.67:
             //   Mean Squared Error: 0.79
             //   Root Mean Squared Error: 0.89
-            //   RSquared: 0.61 (closer to 1 is better. The worest case is 0)";
+            //   RSquared: 0.61 (closer to 1 is better. The worst case is 0)";
 #>

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorizationWithOptions.cs

@@ -34,13 +34,13 @@ public static void Example()
             // Define trainer options.
             var options = new MatrixFactorizationTrainer.Options
             {
-                // Specify IDataView colum which stores matrix column indexes. 
+                // Specify IDataView column which stores matrix column indexes. 
                 MatrixColumnIndexColumnName = nameof(MatrixElement.MatrixColumnIndex
                     ),
 
-                // Specify IDataView colum which stores matrix row indexes. 
+                // Specify IDataView column which stores matrix row indexes. 
                 MatrixRowIndexColumnName = nameof(MatrixElement.MatrixRowIndex),
-                // Specify IDataView colum which stores matrix elements' values. 
+                // Specify IDataView column which stores matrix elements' values. 
                 LabelColumnName = nameof(MatrixElement.Value),
                 // Time of going through the entire data set once.
                 NumberOfIterations = 10,
@@ -95,7 +95,7 @@ public static void Example()
             //   Mean Absolute Error: 0.18
             //   Mean Squared Error: 0.05
             //   Root Mean Squared Error: 0.23
-            //   RSquared: 0.97 (closer to 1 is better. The worest case is 0)
+            //   RSquared: 0.97 (closer to 1 is better. The worst case is 0)
         }
 
         // The following variables are used to define the shape of the example

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorizationWithOptions.tt

@@ -13,13 +13,13 @@ string ExtraUsing = "using Microsoft.ML.Trainers;";
 string Trainer = "MatrixFactorization";
 string TrainerOptions = @"MatrixFactorizationTrainer.Options
             {
-                // Specify IDataView colum which stores matrix column indexes. 
+                // Specify IDataView column which stores matrix column indexes. 
                 MatrixColumnIndexColumnName = nameof(MatrixElement.MatrixColumnIndex
                     ),
 
-                // Specify IDataView colum which stores matrix row indexes. 
+                // Specify IDataView column which stores matrix row indexes. 
                 MatrixRowIndexColumnName = nameof(MatrixElement.MatrixRowIndex),
-                // Specify IDataView colum which stores matrix elements' values. 
+                // Specify IDataView column which stores matrix elements' values. 
                 LabelColumnName = nameof(MatrixElement.Value),
                 // Time of going through the entire data set once.
                 NumberOfIterations = 10,
@@ -46,5 +46,5 @@ string ExpectedOutput = @"// Expected output:
             //   Mean Absolute Error: 0.18
             //   Mean Squared Error: 0.05
             //   Root Mean Squared Error: 0.23
-            //   RSquared: 0.97 (closer to 1 is better. The worest case is 0)";
+            //   RSquared: 0.97 (closer to 1 is better. The worst case is 0)";
 #>

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastForest.cs

@@ -66,7 +66,7 @@ public static void Example()
             //   Mean Absolute Error: 0.06
             //   Mean Squared Error: 0.00
             //   Root Mean Squared Error: 0.07
-            //   RSquared: 0.96 (closer to 1 is better. The worest case is 0)
+            //   RSquared: 0.96 (closer to 1 is better. The worst case is 0)
         }
 
         private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastForest.tt

@@ -24,5 +24,5 @@ string ExpectedOutput = @"// Expected output:
             //   Mean Absolute Error: 0.06
             //   Mean Squared Error: 0.00
             //   Root Mean Squared Error: 0.07
-            //   RSquared: 0.96 (closer to 1 is better. The worest case is 0)";
+            //   RSquared: 0.96 (closer to 1 is better. The worst case is 0)";
 #>

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastForestWithOptions.cs

@@ -79,7 +79,7 @@ public static void Example()
             //   Mean Absolute Error: 0.06
             //   Mean Squared Error: 0.01
             //   Root Mean Squared Error: 0.07
-            //   RSquared: 0.95 (closer to 1 is better. The worest case is 0)
+            //   RSquared: 0.95 (closer to 1 is better. The worst case is 0)
         }
 
         private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastForestWithOptions.tt

@@ -31,5 +31,5 @@ string ExpectedOutput = @"// Expected output:
             //   Mean Absolute Error: 0.06
             //   Mean Squared Error: 0.01
             //   Root Mean Squared Error: 0.07
-            //   RSquared: 0.95 (closer to 1 is better. The worest case is 0)";
+            //   RSquared: 0.95 (closer to 1 is better. The worst case is 0)";
 #>

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastTree.cs

@@ -66,7 +66,7 @@ public static void Example()
             //   Mean Absolute Error: 0.03
             //   Mean Squared Error: 0.00
             //   Root Mean Squared Error: 0.03
-            //   RSquared: 0.99 (closer to 1 is better. The worest case is 0)
+            //   RSquared: 0.99 (closer to 1 is better. The worst case is 0)
         }
 
         private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastTree.tt

@@ -23,6 +23,6 @@ string ExpectedOutput = @"// Expected output:
             //   Mean Absolute Error: 0.03
             //   Mean Squared Error: 0.00
             //   Root Mean Squared Error: 0.03
-            //   RSquared: 0.99 (closer to 1 is better. The worest case is 0)";
+            //   RSquared: 0.99 (closer to 1 is better. The worst case is 0)";
 
 #>

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastTreeTweedie.cs

@@ -66,7 +66,7 @@ public static void Example()
             //   Mean Absolute Error: 0.04
             //   Mean Squared Error: 0.00
             //   Root Mean Squared Error: 0.06
-            //   RSquared: 0.96 (closer to 1 is better. The worest case is 0)
+            //   RSquared: 0.96 (closer to 1 is better. The worst case is 0)
         }
 
         private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastTreeTweedie.tt

@@ -23,6 +23,6 @@ string ExpectedOutput = @"// Expected output:
             //   Mean Absolute Error: 0.04
             //   Mean Squared Error: 0.00
             //   Root Mean Squared Error: 0.06
-            //   RSquared: 0.96 (closer to 1 is better. The worest case is 0)";
+            //   RSquared: 0.96 (closer to 1 is better. The worst case is 0)";
 
 #>